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                  <a class="navbar-brand" href="index.html">ANN Benchmarks</a>
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                    <li class="active"><a href="index.html">Home</a></li>
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                    <li class="active"><a href="index.html#datasets">Datasets</a></li>
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                    <li class="active"><a href="index.html#algorithms">Algorithms</a></li>
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            <h1>Info</h1>
            <p>ANN-Benchmarks is a benchmarking environment for approximate nearest neighbor algorithms search. This website contains the current benchmarking results. Please visit <a href="http://github.com/erikbern/ann-benchmarks/">http://github.com/erikbern/ann-benchmarks/</a> to get an overview over evaluated data sets and algorithms. Make a pull request on <a href="http://github.com/erikbern/ann-benchmarks/">Github</a> to add your own code or improvements to the
            benchmarking system.
            </p>
            <div id="results">
            <h1>Benchmarking Results</h1>
            <p>Results are split by distance measure and dataset. In the bottom, you can find an overview of an algorithm's performance on all datasets. Each dataset is annoted
            by <em>(k = ...)</em>, the number of nearest neighbors an algorithm was supposed to return. The plot shown depicts <em>Recall</em> (the fraction
            of true nearest neighbors found, on average over all queries) against <em>Queries per second</em>.  Clicking on a plot reveils detailled interactive plots, including
            approximate recall, index size, and build time.</p>
                                                                        <h2>Benchmarks for Single Queries</h2>
                    
                    <h2 id ="datasets">Results by Dataset</h2>
                                            <h3>Distance: Euclidean </h3>
                                                    <a href="./sift-128-euclidean_10_euclidean.html">
                            <div class="row" id="sift-128-euclidean_10_euclidean">
                                <div class = "col-md-4 bg-success">
                                    <h4>sift-128-euclidean (k = 10)</h4>
                            </div>
                            <div class = "col-md-8">
                                <img class = "img-responsive" src="sift-128-euclidean_10_euclidean.png" />
                            </div>
                        </div>
                        </a>
                        <hr />
                                                                <h2 id="algorithms">Results by Algorithm</h2>
                    <ul class="list-inline"><b>Algorithms:</b>
                                                    <li><a href="#opengauss-hnsw">opengauss-hnsw</a></li>
                                                    <li><a href="#opengauss-ivf">opengauss-ivf</a></li>
                                                    <li><a href="#opengauss-ivf-pq">opengauss-ivf-pq</a></li>
                                            </ul>
                                        <a href="./opengauss-hnsw.html">
                        <div class="row" id="opengauss-hnsw">
                            <div class = "col-md-4 bg-success">
                                <h4>opengauss-hnsw</h4>
                        </div>
                        <div class = "col-md-8">
                            <img class = "img-responsive" src="opengauss-hnsw.png" />
                        </div>
                    </div>
                    </a>
                    <hr />
                                        <a href="./opengauss-ivf.html">
                        <div class="row" id="opengauss-ivf">
                            <div class = "col-md-4 bg-success">
                                <h4>opengauss-ivf</h4>
                        </div>
                        <div class = "col-md-8">
                            <img class = "img-responsive" src="opengauss-ivf.png" />
                        </div>
                    </div>
                    </a>
                    <hr />
                                        <a href="./opengauss-ivf-pq.html">
                        <div class="row" id="opengauss-ivf-pq">
                            <div class = "col-md-4 bg-success">
                                <h4>opengauss-ivf-pq</h4>
                        </div>
                        <div class = "col-md-8">
                            <img class = "img-responsive" src="opengauss-ivf-pq.png" />
                        </div>
                    </div>
                    </a>
                    <hr />
                                                                            
            <div id="contact">
            <h2>Contact</h2>
            <p>ANN-Benchmarks has been developed by Martin Aumueller (maau@itu.dk), Erik Bernhardsson (mail@erikbern.com), and Alec Faitfull (alef@itu.dk). Please use
            <a href="https://github.com/erikbern/ann-benchmarks/">Github</a> to submit your implementation or improvements.</p>
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